class_docstr: See :class:`torch_geometric.nn.conv.GraphConv`.
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      aggr: str = "add",
      bias: bool = True,
  ):
      assert aggr in ["add", "mean", "max"]
      super().__init__()
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.aggr = aggr
  
      self.lin_rel = Linear(in_channels, out_channels, bias=bias)
      self.lin_root = Linear(in_channels, out_channels, bias=False)
  
      self.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/dense/dense_graph_conv.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch.nn import Linear
label_desc: Use image node_id 1 to create a new DenseGraphConv object from inherited base classes: torch.nn with example: obj = DenseGraphConv(in_channels, out_channels, aggr, bias)